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Milani NBL, García-Cicourel AR, Blomberg J, Edam R, Samanipour S, Bos TS, Pirok BWJ. Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography. Anal Chim Acta 2024; 1312:342724. [PMID: 38834259 DOI: 10.1016/j.aca.2024.342724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/22/2024] [Accepted: 05/14/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND Comprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. RESULT To tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SIGNIFICANCE Data analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.
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Affiliation(s)
- Nino B L Milani
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.
| | | | - Jan Blomberg
- Shell Global Solutions International B.V., Grasweg 31, 1031 HW, Amsterdam, the Netherlands
| | - Rob Edam
- Shell Global Solutions International B.V., Grasweg 31, 1031 HW, Amsterdam, the Netherlands
| | - Saer Samanipour
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Tijmen S Bos
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Bob W J Pirok
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.
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Milani NBL, van Gilst E, Pirok BWJ, Schoenmakers PJ. Comprehensive two-dimensional gas chromatography- A discussion on recent innovations. J Sep Sci 2023; 46:e2300304. [PMID: 37654057 DOI: 10.1002/jssc.202300304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/16/2023] [Accepted: 08/19/2023] [Indexed: 09/02/2023]
Abstract
Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.
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Affiliation(s)
- Nino B L Milani
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
| | - Eric van Gilst
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
| | - Bob W J Pirok
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
| | - Peter J Schoenmakers
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
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Safari-Alighiarloo N, Mani-Varnosfaderani A, Madani NH, Tabatabaei SM, Babaei MR, Khamseh ME. Potential metabolic biomarkers of critical limb ischemia in people with type 2 diabetes mellitus. Metabolomics 2023; 19:66. [PMID: 37452163 DOI: 10.1007/s11306-023-02029-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023]
Abstract
INTRODUCTION Type 2 diabetes mellitus (T2DM) is a significant risk factor for the development of critical limb ischemia (CLI), the most advanced stage of peripheral arterial disease. The concurrent existence of T2DM and CLI often leads to adverse outcomes, namely limb amputation. OBJECTIVE To identify biomarkers for improving the screening of CLI in high-risk people with T2DM. METHODS We investigated metabolome profiles in serum samples of 113 T2DM people with CLI (n = 23, G2) and without CLI (n = 45, G0: no lower limb stenosis (LLS) and n = 45, G1: LLS < 50%), using hydrogen nuclear magnetic resonance (1H NMR) approach. Principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to analyze 1H NMR data. RESULTS Twenty potential metabolites that could discriminate people with T2DM and CLI (G2) from non-CLI patients without LLS (G0) were determined in serum samples. The correct percent of classification for the PLS-DA model for the test set samples was 85% (n = 20) and 100% (n = 5) for G0 and G2 groups, respectively. Non-CLI patients with LLS < 50% (G1) were projected on the PCA abstract space built using 20 discriminatory metabolites. Eleven people with T2DM and LLS < 50% were prospectively followed, and their ankle-brachial index (ABI) was measured after 4 years. A promising agreement existed between the PCA model's predictions and those obtained by ABI values. CONCLUSION The findings suggest that confirmation of blood potential metabolic biomarkers as a complement to ABI for screening of CLI in a large group of high-risk people with T2DM is needed.
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Affiliation(s)
- Nahid Safari-Alighiarloo
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran.
- Endocrine and Metabolism Research Institute, Firoozeh Alley, Valiasr Square, Tehran, Iran.
| | - Ahmad Mani-Varnosfaderani
- Chemometrics and Cheminformatics Laboratory, Department of Chemistry, Tarbiat Modares University, Tehran, Iran.
| | - Nahid Hashemi Madani
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran
| | - Seyyed Mohammad Tabatabaei
- Medical Informatics Department, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Reza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Science, Tehran, Iran
| | - Mohammad E Khamseh
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran
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Perumal AB, Nambiar RB, Luo X, Su Z, Li X, He Y. Exploring dynamic changes of fungal cellular components during nanoemulsion treatment by multivariate microRaman imaging. Talanta 2023; 261:124666. [PMID: 37210918 DOI: 10.1016/j.talanta.2023.124666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 05/23/2023]
Abstract
Recently, essential oils (EO) have gained a lot of interest for use as antifungal agent in food and agricultural industry and extensive research is ongoing to understand their mode of action. However, the exact mechanism is not yet elucidated. Here, we integrated spectral unmixing and Raman microspectroscopy imaging to unveil the antifungal mechanism of green tea EO based nanoemulsion (NE) against Magnaporthe oryzae. The dramatic change in protein, lipid, adenine, and guanine bands indicate that NE has a significant impact on the protein, lipid and metabolic processes of purine. The results also demonstrated that the NE treatment caused damage to fungal hyphae by inducing a physical injury leading to cell wall damage and loss of integrity. Our study shows that MCR-ALS (Multivariate Curve Resolution-Alternating Least Squares) and N-FINDR (N-finder algorithm) Raman imaging could serve as a suitable complementary package to the traditional methods, for revealing the antifungal mechanism of action of EO/NE.
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Affiliation(s)
- Anand Babu Perumal
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
| | - Reshma B Nambiar
- College of Animal Science, Zhejiang University, Hangzhou, 310058, China.
| | - Xuelun Luo
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Zhenzhu Su
- State Key Laboratory for Rice Biology, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
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Critical comparison of background correction algorithms used in chromatography. Anal Chim Acta 2022; 1201:339605. [DOI: 10.1016/j.aca.2022.339605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 11/19/2022]
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Feizi N, Hashemi-Nasab FS, Golpelichi F, Saburouh N, Parastar H. Recent trends in application of chemometric methods for GC-MS and GC×GC-MS-based metabolomic studies. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116239] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Li X, Sha J, Xia Y, Sheng K, Liu Y, He Y. Quantitative visualization of subcellular lignocellulose revealing the mechanism of alkali pretreatment to promote methane production of rice straw. BIOTECHNOLOGY FOR BIOFUELS 2020; 13:8. [PMID: 31988660 PMCID: PMC6966900 DOI: 10.1186/s13068-020-1648-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 01/02/2020] [Indexed: 05/07/2023]
Abstract
BACKGROUND As a renewable carbon source, biomass energy not only helps in resolving the management problems of lignocellulosic wastes, but also helps to alleviate the global climate change by controlling environmental pollution raised by their generation on a large scale. However, the bottleneck problem of extensive production of biofuels lies in the filamentous crystal structure of cellulose and the embedded connection with lignin in biomass that leads to poor accessibility, weak degradation and digestion by microorganisms. Some pretreatment methods have shown significant improvement of methane yield and production rate, but the promotion mechanism has not been thoroughly studied. Revealing the temporal and spatial effects of pretreatment on lignocellulose will greatly help deepen our understanding of the optimization mechanism of pretreatment, and promote efficient utilization of lignocellulosic biomass. Here, we propose an approach for qualitative, quantitative, and location analysis of subcellular lignocellulosic changes induced by alkali treatment based on label-free Raman microspectroscopy combined with chemometrics. RESULTS Firstly, the variations of rice straw induced by alkali treatment were characterized by the Raman spectra, and the Raman fingerprint characteristics for classification of rice straw were captured. Then, a label-free Raman chemical imaging strategy was executed to obtain subcellular distribution of the lignocellulose, in the strategy a serious interference of plant tissues' fluorescence background was effectively removed. Finally, the effects of alkali pretreatment on the subcellular spatial distribution of lignocellulose in different types of cells were discovered. CONCLUSIONS The results demonstrated the mechanism of alkali treatment that promotes methane production in rice straw through anaerobic digestion by means of a systemic study of the evidence from the macroscopic measurement and Raman microscopic quantitative and localization two-angle views. Raman chemical imaging combined with chemometrics could nondestructively realize qualitative, quantitative, and location analysis of the lignocellulose of rice straw at a subcellular level in a label-free way, which was beneficial to optimize pretreatment for the improvement of biomass conversion efficiency and promote extensive utilization of biofuel.
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Affiliation(s)
- Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Areas, 866 Yuhangtang Road, Hangzhou, 310058 China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
| | - Junjing Sha
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Areas, 866 Yuhangtang Road, Hangzhou, 310058 China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
| | - Yihua Xia
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Areas, 866 Yuhangtang Road, Hangzhou, 310058 China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
| | - Kuichuan Sheng
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Areas, 866 Yuhangtang Road, Hangzhou, 310058 China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Areas, 866 Yuhangtang Road, Hangzhou, 310058 China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Areas, 866 Yuhangtang Road, Hangzhou, 310058 China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058 China
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Liu Y, Lin J. A general-purpose signal processing algorithm for biological profiles using only first-order derivative information. BMC Bioinformatics 2019; 20:611. [PMID: 31775621 PMCID: PMC6882060 DOI: 10.1186/s12859-019-3188-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 11/04/2019] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Automatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences. RESULTS We developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted. CONCLUSIONS We have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.
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Affiliation(s)
- Yuanjie Liu
- College of Information and Electrical Engineering, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China.
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China.
| | - Jianhan Lin
- College of Information and Electrical Engineering, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China
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Chen L, Wu Y, Li T, Chen Z. Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2018; 2018:9031356. [PMID: 30245903 PMCID: PMC6136554 DOI: 10.1155/2018/9031356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 07/08/2018] [Accepted: 07/26/2018] [Indexed: 06/08/2023]
Abstract
Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually.
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Affiliation(s)
- Long Chen
- Faculty of Science and Technology, University of Macau, E11 Avenida da Universidade, Taipa, Macau
| | - Yingwen Wu
- Faculty of Science and Technology, University of Macau, E11 Avenida da Universidade, Taipa, Macau
| | - Tianjun Li
- Faculty of Science and Technology, University of Macau, E11 Avenida da Universidade, Taipa, Macau
| | - Zhuo Chen
- Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha 410082, China
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Kanginejad A, Mani-Varnosfaderani A. Chemometrics advances on the challenges of the gas chromatography–mass spectrometry metabolomics data: a review. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2018. [DOI: 10.1007/s13738-018-1461-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Chen Y, Cai K, Tu Z, Nie W, Ji T, Hu B, Chen C, Jiang S. Prediction of benzo[a]pyrene content of smoked sausage using back-propagation artificial neural network. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2018; 98:3022-3030. [PMID: 29193124 DOI: 10.1002/jsfa.8801] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 10/15/2017] [Accepted: 11/24/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Benzo[a]pyrene (BaP), a potent mutagen and carcinogen, is reported to be present in processed meat products and, in particular, in smoked meat. However, few methods exist for predictive determination of the BaP content of smoked meats such as sausage. In this study, an artificial neural network (ANN) model based on the back-propagation (BP) algorithm was used to predict the BaP content of smoked sausage. RESULTS The results showed that the BP network based on the Levenberg-Marquardt algorithm was the best suited for creating a nonlinear map between the input and output parameters. The optimal network structure was 3-7-1 and the learning rate was 0.6. This BP-ANN model allowed for accurate predictions, with the correlation coefficients (R) for the experimentally determined training, validation, test and global data sets being 0.94, 0.96, 0.95 and 0.95 respectively. The validation performance was 0.013, suggesting that the proposed BP-ANN may be used to predictively detect the BaP content of smoked meat products. CONCLUSION An effective predictive model was constructed for estimation of the BaP content of smoked sausage using ANN modeling techniques, which shows potential to predict the BaP content in smoked sausage. © 2017 Society of Chemical Industry.
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Affiliation(s)
- Yan Chen
- School of Food Science and Engineering, Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei University of Technology, Hefei, China
| | - Kezhou Cai
- School of Food Science and Engineering, Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei University of Technology, Hefei, China
| | - Zehui Tu
- School of Food Science and Engineering, Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei University of Technology, Hefei, China
| | - Wen Nie
- School of Food Science and Engineering, Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei University of Technology, Hefei, China
| | - Tuo Ji
- School of Food Science and Engineering, Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei University of Technology, Hefei, China
| | - Bing Hu
- Anhui Grain & Oil Quality Inspection Station, China National Supervision and Examination Center For Foodstuff Quality, Hefei, China
| | - Conggui Chen
- School of Food Science and Engineering, Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei University of Technology, Hefei, China
| | - Shaotong Jiang
- School of Food Science and Engineering, Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei University of Technology, Hefei, China
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